diff options
Diffstat (limited to 'lab-windows/grid_scope.ipynb')
| -rw-r--r-- | lab-windows/grid_scope.ipynb | 236 |
1 files changed, 131 insertions, 105 deletions
diff --git a/lab-windows/grid_scope.ipynb b/lab-windows/grid_scope.ipynb index f3e9176..1ddde37 100644 --- a/lab-windows/grid_scope.ipynb +++ b/lab-windows/grid_scope.ipynb @@ -36,7 +36,7 @@ "metadata": {}, "outputs": [], "source": [ - "db = sqlite3.connect('data/waveform-raspi.sqlite3')" + "db = sqlite3.connect('data/waveform-raspi-2-2.sqlite3')" ] }, { @@ -48,7 +48,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Run 000: 2020-01-31 19:05:24 - 2020-02-01 01:13:45 ( 6:08:21.589, 22126080sp)\n" + "Run 000: 2020-03-25 16:07:36 - 2020-03-26 00:15:13 ( 8:07:37.266, 29261120sp)\n" ] } ], @@ -100,12 +100,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b088957d9a24a74ab1b81d68099aa99", + "model_id": "582c4360e293466e9baed5bc66a47883", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=691440.0), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=914410.0), HTML(value='')))" ] }, "metadata": {}, @@ -133,7 +133,7 @@ " 'SELECT seq, data FROM measurements WHERE run_id = ? ORDER BY rx_ts LIMIT ? OFFSET ?',\n", " (last_run, limit, n_records-limit))), total=n_records):\n", " \n", - " if last_seq is None or seq == (last_seq + 1)%0xffff:\n", + " if last_seq is None or seq == (last_seq + 1)%0x10000:\n", " last_seq = seq\n", " idx = write_index if skip_dropped_sections else i\n", " data[idx*record_size:(idx+1)*record_size] = np.frombuffer(chunk, dtype='<H')\n", @@ -156,7 +156,7 @@ { "data": { "text/plain": [ - "227.68691180713367" + "227.0922848236977" ] }, "execution_count": 7, @@ -177,7 +177,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a6f7e9ac8f04d1b84035c29623cfa99", + "model_id": "ecf3e3e261c54d87b169c1eb391a67f9", "version_major": 2, "version_minor": 0 }, @@ -266,7 +266,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a3824e732a0647d4a12e8c5a567f5dc0", + "model_id": "cc101709475d440ea77e68bcb56ce3b7", "version_major": 2, "version_minor": 0 }, @@ -299,12 +299,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "342f89b5d55f435ab132deb5d051c926", + "model_id": "b0202d59643548cf83b6fa6fd7580d2d", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=221260.0), HTML(value='')))" + "HBox(children=(FloatProgress(value=0.0, max=292611.0), HTML(value='')))" ] }, "metadata": {}, @@ -364,7 +364,7 @@ " #print(coeff)\n", " A, mu, sigma, *_ = coeff\n", " f_mean[le_t] = mu\n", - " except Exception:\n", + " except Exception as e:\n", " f_mean[le_t] = np.nan" ] }, @@ -376,7 +376,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "33431729d479469cb89bcf86e22508ad", + "model_id": "59d50a2876634433bdfb751a7a66d9ca", "version_major": 2, "version_minor": 0 }, @@ -420,6 +420,8 @@ "for i in np.where(np.isnan(f_mean))[0]:\n", " ax.axvspan(f_t[i], f_t[i+1], color='lightblue')\n", "\n", + "formatter = matplotlib.ticker.FuncFormatter(lambda s, x: str(datetime.timedelta(seconds=s)))\n", + "ax.xaxis.set_major_formatter(formatter)\n", "ax.set_xlabel('recording time t [s]')\n", "None" ] @@ -432,7 +434,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e8a4ecab028f4fcf8d9821dd66a42027", + "model_id": "95cfea97c3b946ed9cdf351b16c2c45e", "version_major": 2, "version_minor": 0 }, @@ -529,13 +531,21 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 53, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-53-c54c3e4ac2be>:20: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " fig, ax = plt.subplots(figsize=(5, 2))\n" + ] + }, + { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bcf4b384d95949c38a4754cdb47ae512", + "model_id": "ad5454f664734681adb8640f480ce69a", "version_major": 2, "version_minor": 0 }, @@ -547,63 +557,54 @@ "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/user/safety-reset/lab-windows/env/lib/python3.8/site-packages/numpy/core/_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return array(a, dtype, copy=False, order=order)\n" - ] - }, - { "data": { "text/plain": [ - "5.0" + "Text(20, 1, '50 Hz')" ] }, - "execution_count": 16, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data = f_copy\n", - "ys = scipy.fftpack.fft(data)\n", - "ys = scipy.fftpack.fftshift(ys)\n", - "#ys = 2.0/len(data) * np.abs(ys[:len(data)//2])\n", - "#s = 3\n", - "\n", - "#ys = np.convolve(ys, np.ones((s,))/s, mode='valid')\n", - "\n", - "#xs = np.linspace(0, 5, len(data)//2)\n", - "xs = np.linspace(-5, 5, len(data))\n", + "# Number of samplepoints\n", + "N = len(data)\n", + "# sample spacing\n", + "T = 1.0 / sampling_rate\n", + "x = np.linspace(0.0, N*T, N)\n", + "yf = scipy.fftpack.fft(data * sig.blackman(N))\n", + "xf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n", "\n", - "#ys *= 2*np.pi*xs[s//2:-s//2+1]\n", - "#ys *= xs\n", + "yf = 2.0/N * np.abs(yf[:N//2])\n", "\n", - "#xs = np.linspace(len(data)/2, 1, len(data)/2)\n", + "average_from = lambda val, start, average_width: np.hstack([val[:start], [ np.mean(val[i:i+average_width]) for i in range(start, len(val), average_width) ]])\n", "\n", - "fig, ax = plt.subplots(figsize=(9,5))\n", - "#ax.loglog(xs[s//2:-s//2+1], ys)\n", - "#ax.loglog(xs[s//2:-s//2+1], ys)\n", - "#ax.loglog(xs, ys)\n", - "#ys[len(xs)//2] = 0\n", - "#ax.set_yscale('log')\n", - "ax.plot(xs, ys)\n", - "#ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n", - "ax.grid()\n", - "#plt.show()\n", - "xs[-1]" + "average_width = 6\n", + "average_start = 20\n", + "yf = average_from(yf, average_start, average_width)\n", + "xf = average_from(xf, average_start, average_width)\n", + "yf = average_from(yf, 200, average_width)\n", + "xf = average_from(xf, 200, average_width)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 68, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-68-21b49a5af249>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " fig, ax = plt.subplots(figsize=(6, 3))\n" + ] + }, + { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cbc03380a4234b4d8160d25e867ff5a9", + "model_id": "4a3edb0925fe47eb8751150aa7da8c22", "version_major": 2, "version_minor": 0 }, @@ -615,54 +616,51 @@ "output_type": "display_data" }, { - "data": { - "text/plain": [ - "(1.6666666666666667e-05, 0.5)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] } ], "source": [ - "# Number of samplepoints\n", - "N = len(data)\n", - "# sample spacing\n", - "T = 1.0 / 10.0\n", - "x = np.linspace(0.0, N*T, N)\n", - "yf = scipy.fftpack.fft(data)\n", - "xf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n", - "\n", - "yf = 2.0/N * np.abs(yf[:N//2])\n", - "\n", - "average_from = lambda val, start, average_width: np.hstack([val[:start], [ np.mean(val[i:i+average_width]) for i in range(start, len(val), average_width) ]])\n", - "\n", - "average_width = 6\n", - "average_start = 20\n", - "yf = average_from(yf, average_start, average_width)\n", - "xf = average_from(xf, average_start, average_width)\n", - "yf = average_from(yf, 200, average_width)\n", - "xf = average_from(xf, 200, average_width)\n", - "\n", - "fig, ax = plt.subplots()\n", + "fig, ax = plt.subplots(figsize=(6, 3))\n", + "fig.tight_layout()\n", "ax.loglog(xf, yf)\n", - "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n", - "ax.set_xlabel('T in s')\n", - "ax.set_ylabel('Amplitude Δf')\n", + "#ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n", + "ax.set_xlabel('f [Hz]')\n", + "ax.set_ylabel('Amplitude V [V]')\n", "ax.grid()\n", - "ax.set_xlim([1/60000, 0.5])" + "ax.set_xlim([0.1, 500])\n", + "fig.subplots_adjust(bottom=0.2)\n", + "\n", + "for le_f in (50, 150, 250, 350, 450):\n", + " ax.axvline(le_f, color=(1, 0.5, 0.5), zorder=-2)\n", + "ax.annotate('50 Hz', xy=(20, 1), xycoords='data', bbox=dict(fc='white', alpha=0.8, ec='none'))\n", + "font = {'family' : 'normal',\n", + " 'weight' : 'normal',\n", + " 'size' : 10}\n", + "matplotlib.rc('font', **font)\n", + "fig.savefig('fig_out/mains_voltage_spectrum.eps')" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 43, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-43-2e31f0cb9460>:21: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " fig, ax = plt.subplots()\n" + ] + }, + { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8eb3621b74c04c4fb7dd3e919f0d1fed", + "model_id": "37e159955f114f36824dd42da060b9ea", "version_major": 2, "version_minor": 0 }, @@ -676,55 +674,83 @@ { "data": { "text/plain": [ - "(5e-07, 0.02)" + "[<matplotlib.lines.Line2D at 0x7fafc108cdc0>]" ] }, - "execution_count": 18, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Number of samplepoints\n", - "N = len(data)\n", + "newcopy = np.copy(f_mean[1:-2])\n", + "N = len(newcopy)\n", "# sample spacing\n", - "T = 1.0 / 10.0\n", + "T = 1.0 / 10\n", "x = np.linspace(0.0, N*T, N)\n", - "yf = scipy.fftpack.fft(data)\n", - "xf = np.linspace(0.0, 1.0/(2.0*T), N//2)\n", + "yf = scipy.fftpack.fft(newcopy * sig.blackman(N))\n", + "xf = np.linspace(0.0, 10/2, N//2)\n", "\n", "yf = 2.0/N * np.abs(yf[:N//2])\n", "\n", "average_from = lambda val, start, average_width: np.hstack([val[:start], [ np.mean(val[i:i+average_width]) for i in range(start, len(val), average_width) ]])\n", "\n", - "average_width = 6\n", - "average_start = 20\n", - "yf = average_from(yf, average_start, average_width)\n", - "xf = average_from(xf, average_start, average_width)\n", - "yf = average_from(yf, 70, 4)\n", - "xf = average_from(xf, 70, 4)\n", + "average_width1, average_start1 = 3, 40\n", + "average_width2, average_start2 = 4, 100\n", + "yf = average_from(yf, average_start1, average_width1)\n", + "xf = average_from(xf, average_start1, average_width1)\n", + "yf = average_from(yf, average_start2, average_width2)\n", + "xf = average_from(xf, average_start2, average_width2)\n", "\n", "fig, ax = plt.subplots()\n", "ax.loglog(xf, yf)\n", "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: f'{1/x:.1f}'))\n", - "ax.set_xlabel('T in s')\n", - "ax.set_ylabel('Amplitude Δf')\n", + "ax.set_xlabel('T [s]')\n", + "ax.set_ylabel('Amplitude Δf [Hz]')\n", "\n", - "for i, t in enumerate([45, 60, 600, 1200, 1800, 3600]):\n", + "for i, t in enumerate([60, 300, 450, 1200, 1800]):\n", " ax.axvline(1/t, color='red', alpha=0.5)\n", " ax.annotate(f'{t} s', xy=(1/t, 3e-5), xytext=(-15, 0), xycoords='data', textcoords='offset pixels', rotation=90)\n", "#ax.text(1/60, 10,'60 s', ha='left')\n", "ax.grid()\n", - "ax.set_xlim([1/60000, 0.5])\n", - "ax.set_ylim([5e-7, 2e-2])" + "#ax.set_xlim([1/60000, 0.5])\n", + "ax.set_ylim([5e-7, 2e-2])\n", + "ax.plot(xf[1:], 2e-6/xf[1:])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "671ae919bf124e72b54144310ea1602d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(np.linspace(0, (len(f_mean)-3)/10, len(f_mean)-3) , f_mean[1:-2])\n", + "ax.grid()" ] } ], "metadata": { "kernelspec": { - "display_name": "winlabenv", + "display_name": "labenv", "language": "python", - "name": "winlabenv" + "name": "labenv" }, "language_info": { "codemirror_mode": { @@ -736,7 +762,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.2" } }, "nbformat": 4, |